Search Results for author: Devavrat Shah

Found 71 papers, 8 papers with code

Bayesian regression and Bitcoin

15 code implementations6 Oct 2014 Devavrat Shah, Kang Zhang

In this paper, we discuss the method of Bayesian regression and its efficacy for predicting price variation of Bitcoin, a recently popularized virtual, cryptographic currency.

Bayesian Inference Binary Classification +2

Model Agnostic Time Series Analysis via Matrix Estimation

1 code implementation25 Feb 2018 Anish Agarwal, Muhammad Jehangir Amjad, Devavrat Shah, Dennis Shen

In effect, this generalizes the widely used Singular Spectrum Analysis (SSA) in time series literature, and allows us to establish a rigorous link between time series analysis and matrix estimation.

Imputation regression +2

Robust Synthetic Control

1 code implementation18 Nov 2017 Muhammad Jehangir Amjad, Devavrat Shah, Dennis Shen

Our experiments, using both real-world and synthetic datasets, demonstrate that our robust generalization yields an improvement over the classical synthetic control method.

counterfactual

CausalSim: A Causal Framework for Unbiased Trace-Driven Simulation

1 code implementation5 Jan 2022 Abdullah Alomar, Pouya Hamadanian, Arash Nasr-Esfahany, Anish Agarwal, Mohammad Alizadeh, Devavrat Shah

Key to CausalSim is mapping unbiased trace-driven simulation to a tensor completion problem with extremely sparse observations.

Causal Inference

Counterfactual Identifiability of Bijective Causal Models

1 code implementation4 Feb 2023 Arash Nasr-Esfahany, Mohammad Alizadeh, Devavrat Shah

We study counterfactual identifiability in causal models with bijective generation mechanisms (BGM), a class that generalizes several widely-used causal models in the literature.

counterfactual

Distinguishing the Indistinguishable: Human Expertise in Algorithmic Prediction

1 code implementation1 Feb 2024 Rohan Alur, Manish Raghavan, Devavrat Shah

Our approach focuses on the use of human judgment to distinguish inputs which `look the same' to any feasible predictive algorithm.

Reducing Crowdsourcing to Graphon Estimation, Statistically

no code implementations23 Mar 2017 Devavrat Shah, Christina Lee Yu

Inferring the correct answers to binary tasks based on multiple noisy answers in an unsupervised manner has emerged as the canonical question for micro-task crowdsourcing or more generally aggregating opinions.

Graphon Estimation

Q-learning with Nearest Neighbors

no code implementations NeurIPS 2018 Devavrat Shah, Qiaomin Xie

In particular, for MDPs with a $d$-dimensional state space and the discounted factor $\gamma \in (0, 1)$, given an arbitrary sample path with "covering time" $ L $, we establish that the algorithm is guaranteed to output an $\varepsilon$-accurate estimate of the optimal Q-function using $\tilde{O}\big(L/(\varepsilon^3(1-\gamma)^7)\big)$ samples.

Q-Learning

Regret Guarantees for Item-Item Collaborative Filtering

no code implementations20 Jul 2015 Guy Bresler, Devavrat Shah, Luis F. Voloch

There is much empirical evidence that item-item collaborative filtering works well in practice.

Collaborative Filtering Matrix Completion

Rank Centrality: Ranking from Pair-wise Comparisons

no code implementations8 Sep 2012 Sahand Negahban, Sewoong Oh, Devavrat Shah

To study the efficacy of the algorithm, we consider the popular Bradley-Terry-Luce (BTL) model (equivalent to the Multinomial Logit (MNL) for pair-wise comparisons) in which each object has an associated score which determines the probabilistic outcomes of pair-wise comparisons between objects.

A Latent Source Model for Patch-Based Image Segmentation

no code implementations6 Oct 2015 George Chen, Devavrat Shah, Polina Golland

Despite the popularity and empirical success of patch-based nearest-neighbor and weighted majority voting approaches to medical image segmentation, there has been no theoretical development on when, why, and how well these nonparametric methods work.

Image Segmentation Medical Image Segmentation +2

Structure learning of antiferromagnetic Ising models

no code implementations NeurIPS 2014 Guy Bresler, David Gamarnik, Devavrat Shah

In this paper we investigate the computational complexity of learning the graph structure underlying a discrete undirected graphical model from i. i. d.

Learning graphical models from the Glauber dynamics

no code implementations28 Oct 2014 Guy Bresler, David Gamarnik, Devavrat Shah

In this paper we consider the problem of learning undirected graphical models from data generated according to the Glauber dynamics.

Learning Mixed Multinomial Logit Model from Ordinal Data

no code implementations NeurIPS 2014 Sewoong Oh, Devavrat Shah

In case of single MNL models (no mixture), computationally and statistically tractable learning from pair-wise comparisons is feasible.

Management Tensor Decomposition

A Latent Source Model for Online Collaborative Filtering

no code implementations NeurIPS 2014 Guy Bresler, George H. Chen, Devavrat Shah

Despite the prevalence of collaborative filtering in recommendation systems, there has been little theoretical development on why and how well it works, especially in the "online" setting, where items are recommended to users over time.

Collaborative Filtering Recommendation Systems

Hardness of parameter estimation in graphical models

no code implementations NeurIPS 2014 Guy Bresler, David Gamarnik, Devavrat Shah

Our proof gives a polynomial time reduction from approximating the partition function of the hard-core model, known to be hard, to learning approximate parameters.

Statistical inference with probabilistic graphical models

no code implementations17 Sep 2014 Angélique Drémeau, Christophe Schülke, Yingying Xu, Devavrat Shah

These are notes from the lecture of Devavrat Shah given at the autumn school "Statistical Physics, Optimization, Inference, and Message-Passing Algorithms", that took place in Les Houches, France from Monday September 30th, 2013, till Friday October 11th, 2013.

A Latent Source Model for Nonparametric Time Series Classification

no code implementations NeurIPS 2013 George H. Chen, Stanislav Nikolov, Devavrat Shah

Our guiding hypothesis is that in many applications, such as forecasting which topics will become trends on Twitter, there aren't actually that many prototypical time series to begin with, relative to the number of time series we have access to, e. g., topics become trends on Twitter only in a few distinct manners whereas we can collect massive amounts of Twitter data.

Classification General Classification +3

Partition-Merge: Distributed Inference and Modularity Optimization

no code implementations24 Sep 2013 Vincent Blondel, Kyomin Jung, Pushmeet Kohli, Devavrat Shah

This paper presents a novel meta algorithm, Partition-Merge (PM), which takes existing centralized algorithms for graph computation and makes them distributed and faster.

Community Detection

Budget-Optimal Task Allocation for Reliable Crowdsourcing Systems

no code implementations17 Oct 2011 David R. Karger, Sewoong Oh, Devavrat Shah

Further, we compare our approach with a more general class of algorithms which can dynamically assign tasks.

Image Classification Optical Character Recognition +1

Leaders, Followers, and Community Detection

no code implementations2 Nov 2010 Dhruv Parthasarathy, Devavrat Shah, Tauhid Zaman

For a large number of popular social networks, it recovers communities with a much higher F1 score than other popular algorithms.

Community Detection

Regret vs. Bandwidth Trade-off for Recommendation Systems

no code implementations15 Oct 2018 Linqi Song, Christina Fragouli, Devavrat Shah

We consider recommendation systems that need to operate under wireless bandwidth constraints, measured as number of broadcast transmissions, and demonstrate a (tight for some instances) tradeoff between regret and bandwidth for two scenarios: the case of multi-armed bandit with context, and the case where there is a latent structure in the message space that we can exploit to reduce the learning phase.

Recommendation Systems

Thy Friend is My Friend: Iterative Collaborative Filtering for Sparse Matrix Estimation

no code implementations NeurIPS 2017 Christian Borgs, Jennifer Chayes, Christina E. Lee, Devavrat Shah

We show that the mean squared error (MSE) of our estimator converges to $0$ at the rate of $O(d^2 (pn)^{-2/5})$ as long as $\omega(d^5 n)$ random entries from a total of $n^2$ entries of $Y$ are observed (uniformly sampled), $\E[Y]$ has rank $d$, and the entries of $Y$ have bounded support.

Collaborative Filtering Community Detection +3

Blind Regression: Nonparametric Regression for Latent Variable Models via Collaborative Filtering

no code implementations NeurIPS 2016 Dogyoon Song, Christina E. Lee, Yihua Li, Devavrat Shah

In contrast with classical regression, the features $x = (x_1(u), x_2(i))$ are not observed, making it challenging to apply standard regression methods to predict the unobserved ratings.

Collaborative Filtering Matrix Completion +2

Computing the Stationary Distribution Locally

no code implementations NeurIPS 2013 Christina E. Lee, Asuman Ozdaglar, Devavrat Shah

In this paper, we provide a novel algorithm that answers whether a chosen state in a MC has stationary probability larger than some $\Delta \in (0, 1)$.

Iterative ranking from pair-wise comparisons

no code implementations NeurIPS 2012 Sahand Negahban, Sewoong Oh, Devavrat Shah

In most settings, in addition to obtaining ranking, finding ‘scores’ for each object (e. g. player’s rating) is of interest to understanding the intensity of the preferences.

Iterative Learning for Reliable Crowdsourcing Systems

no code implementations NeurIPS 2011 David R. Karger, Sewoong Oh, Devavrat Shah

Crowdsourcing systems, in which tasks are electronically distributed to numerous ``information piece-workers'', have emerged as an effective paradigm for human-powered solving of large scale problems in domains such as image classification, data entry, optical character recognition, recommendation, and proofreading.

Image Classification Optical Character Recognition +1

A Data-Driven Approach to Modeling Choice

no code implementations NeurIPS 2009 Vivek Farias, Srikanth Jagabathula, Devavrat Shah

We visit the following fundamental problem: For a `generic model of consumer choice (namely, distributions over preference lists) and a limited amount of data on how consumers actually make decisions (such as marginal preference information), how may one predict revenues from offering a particular assortment of choices?

Econometrics Marketing

Local Rules for Global MAP: When Do They Work ?

no code implementations NeurIPS 2009 Kyomin Jung, Pushmeet Kohli, Devavrat Shah

We consider the question of computing Maximum A Posteriori (MAP) assignment in an arbitrary pair-wise Markov Random Field (MRF).

Local Algorithms for Approximate Inference in Minor-Excluded Graphs

no code implementations NeurIPS 2007 Kyomin Jung, Devavrat Shah

We present a new local approximation algorithm for computing MAP and log-partition function for arbitrary exponential family distribution represented by a finite-valued pair-wise Markov random field (MRF), say G. Our algorithm is based on decomposing G into appropriately chosen small components; computing estimates locally in each of these components and then producing a good global solution.

Understanding & Generalizing AlphaGo Zero

no code implementations ICLR 2019 Ravichandra Addanki, Mohammad Alizadeh, Shaileshh Bojja Venkatakrishnan, Devavrat Shah, Qiaomin Xie, Zhi Xu

AlphaGo Zero (AGZ) introduced a new {\em tabula rasa} reinforcement learning algorithm that has achieved superhuman performance in the games of Go, Chess, and Shogi with no prior knowledge other than the rules of the game.

Decision Making reinforcement-learning +2

Learning RUMs: Reducing Mixture to Single Component via PCA

no code implementations31 Dec 2018 Devavrat Shah, Dogyoon Song

Despite the success of RUMs in various domains and the versatility of mixture RUMs to capture the heterogeneity in preferences, there has been only limited progress in learning a mixture of RUMs from partial data such as pairwise comparisons.

Clustering

Non-Asymptotic Analysis of Monte Carlo Tree Search

no code implementations14 Feb 2019 Devavrat Shah, Qiaomin Xie, Zhi Xu

In effect, we establish that to learn an $\varepsilon$ approximation of the value function with respect to $\ell_\infty$ norm, MCTS combined with nearest neighbor requires a sample size scaling as $\widetilde{O}\big(\varepsilon^{-(d+4)}\big)$, where $d$ is the dimension of the state space.

On Robustness of Principal Component Regression

no code implementations NeurIPS 2019 Anish Agarwal, Devavrat Shah, Dennis Shen, Dogyoon Song

As an important contribution to the Synthetic Control literature, we establish that an (approximate) linear synthetic control exists in the setting of a generalized factor model; traditionally, the existence of a synthetic control needs to be assumed to exist as an axiom.

Art Analysis Causal Inference +3

tspDB: Time Series Predict DB

no code implementations17 Mar 2019 Anish Agarwal, Abdullah Alomar, Devavrat Shah

Computationally, tspDB is 59-62x and 94-95x faster compared to LSTM and DeepAR in terms of median ML model training time and prediction query latency, respectively.

Imputation Prediction Intervals +2

Robust Max Entrywise Error Bounds for Tensor Estimation from Sparse Observations via Similarity Based Collaborative Filtering

no code implementations3 Aug 2019 Devavrat Shah, Christina Lee Yu

We prove that the algorithm recovers a finite rank tensor with maximum entry-wise error (MEE) and mean-squared-error (MSE) decaying to $0$ as long as each entry is observed independently with probability $p = \Omega(n^{-3/2 + \kappa})$ for any arbitrarily small $\kappa > 0$.

Collaborative Filtering

Short and Wide Network Paths

no code implementations1 Nov 2019 Lavanya Marla, Lav R. Varshney, Devavrat Shah, Nirmal A. Prakash, Michael E. Gale

We show this notion of pipelined network flow is optimized using network paths that are both short and wide, and develop efficient algorithms to compute such paths for given pairs of nodes and for all-pairs.

On Reinforcement Learning for Turn-based Zero-sum Markov Games

no code implementations25 Feb 2020 Devavrat Shah, Varun Somani, Qiaomin Xie, Zhi Xu

For a concrete instance of EIS where random policy is used for "exploration", Monte-Carlo Tree Search is used for "policy improvement" and Nearest Neighbors is used for "supervised learning", we establish that this method finds an $\varepsilon$-approximate value function of Nash equilibrium in $\widetilde{O}(\varepsilon^{-(d+4)})$ steps when the underlying state-space of the game is continuous and $d$-dimensional.

reinforcement-learning Reinforcement Learning (RL)

Two Burning Questions on COVID-19: Did shutting down the economy help? Can we (partially) reopen the economy without risking the second wave?

no code implementations30 Apr 2020 Anish Agarwal, Abdullah Alomar, Arnab Sarker, Devavrat Shah, Dennis Shen, Cindy Yang

In essence, the method leverages information from different interventions that have already been enacted across the world and fits it to a policy maker's setting of interest, e. g., to estimate the effect of mobility-restricting interventions on the U. S., we use daily death data from countries that enforced severe mobility restrictions to create a "synthetic low mobility U. S." and predict the counterfactual trajectory of the U. S. if it had indeed applied a similar intervention.

counterfactual

Stable Reinforcement Learning with Unbounded State Space

no code implementations L4DC 2020 Devavrat Shah, Qiaomin Xie, Zhi Xu

As a proof of concept, we propose an RL policy using Sparse-Sampling-based Monte Carlo Oracle and argue that it satisfies the stability property as long as the system dynamics under the optimal policy respects a Lyapunov function.

reinforcement-learning Reinforcement Learning (RL) +1

Sample Efficient Reinforcement Learning via Low-Rank Matrix Estimation

no code implementations NeurIPS 2020 Devavrat Shah, Dogyoon Song, Zhi Xu, Yuzhe Yang

As our key contribution, we develop a simple, iterative learning algorithm that finds $\epsilon$-optimal $Q$-function with sample complexity of $\widetilde{O}(\frac{1}{\epsilon^{\max(d_1, d_2)+2}})$ when the optimal $Q$-function has low rank $r$ and the discounting factor $\gamma$ is below a certain threshold.

Learning Theory reinforcement-learning +1

Estimation of Skill Distributions

no code implementations15 Jun 2020 Ali Jadbabaie, Anuran Makur, Devavrat Shah

In this paper, we study the problem of learning the skill distribution of a population of agents from observations of pairwise games in a tournament.

Density Estimation

Synthetic Interventions

no code implementations13 Jun 2020 Anish Agarwal, Devavrat Shah, Dennis Shen

Towards this, we present a causal framework, synthetic interventions (SI), to infer these $N \times D$ causal parameters while only observing each of the $N$ units under at most two interventions, independent of $D$.

On Multivariate Singular Spectrum Analysis and its Variants

no code implementations24 Jun 2020 Anish Agarwal, Abdullah Alomar, Devavrat Shah

We introduce and analyze a variant of multivariate singular spectrum analysis (mSSA), a popular time series method to impute and forecast a multivariate time series.

Imputation Time Series +1

On Model Identification and Out-of-Sample Prediction of Principal Component Regression: Applications to Synthetic Controls

1 code implementation27 Oct 2020 Anish Agarwal, Devavrat Shah, Dennis Shen

To the best of our knowledge, our prediction guarantees for the fixed design setting have been elusive in both the high-dimensional error-in-variables and synthetic controls literatures.

On Learning Continuous Pairwise Markov Random Fields

no code implementations28 Oct 2020 Abhin Shah, Devavrat Shah, Gregory W. Wornell

We consider learning a sparse pairwise Markov Random Field (MRF) with continuous-valued variables from i. i. d samples.

regression

Gradient-Based Empirical Risk Minimization using Local Polynomial Regression

no code implementations4 Nov 2020 Ali Jadbabaie, Anuran Makur, Devavrat Shah

In contrast, we demonstrate that when the loss function is smooth in the data, we can learn the oracle at every iteration and beat the oracle complexities of both GD and SGD in important regimes.

regression

Estimation of Skill Distribution from a Tournament

no code implementations NeurIPS 2020 Ali Jadbabaie, Anuran Makur, Devavrat Shah

In this paper, we study the problem of learning the skill distribution of a population of agents from observations of pairwise games in a tournament.

Density Estimation

Regret, stability & fairness in matching markets with bandit learners

no code implementations11 Feb 2021 Sarah H. Cen, Devavrat Shah

In this work, we study how competition affects the long-term outcomes of individuals as they learn.

Fairness

PerSim: Data-Efficient Offline Reinforcement Learning with Heterogeneous Agents via Personalized Simulators

no code implementations NeurIPS 2021 Anish Agarwal, Abdullah Alomar, Varkey Alumootil, Devavrat Shah, Dennis Shen, Zhi Xu, Cindy Yang

We consider offline reinforcement learning (RL) with heterogeneous agents under severe data scarcity, i. e., we only observe a single historical trajectory for every agent under an unknown, potentially sub-optimal policy.

Offline RL reinforcement-learning +1

Quantifying Variational Approximation for the Log-Partition Function

no code implementations19 Feb 2021 Romain Cosson, Devavrat Shah

Specifically, we argue that (a variant of) TRW produces an estimate that is within factor $\frac{1}{\sqrt{\kappa(G)}}$ of the true log-partition function for any discrete pairwise graphical model over graph $G$, where $\kappa(G) \in (0, 1]$ captures how far $G$ is from tree structure with $\kappa(G) = 1$ for trees and $2/N$ for the complete graph over $N$ vertices.

Variational Inference

Causal Matrix Completion

no code implementations30 Sep 2021 Anish Agarwal, Munther Dahleh, Devavrat Shah, Dennis Shen

In particular, we establish entry-wise, i. e., max-norm, finite-sample consistency and asymptotic normality results for matrix completion with MNAR data.

Matrix Completion Recommendation Systems

A Computationally Efficient Method for Learning Exponential Family Distributions

no code implementations NeurIPS 2021 Abhin Shah, Devavrat Shah, Gregory W. Wornell

In this work, we propose a computationally efficient estimator that is consistent as well as asymptotically normal under mild conditions.

Time varying regression with hidden linear dynamics

no code implementations29 Dec 2021 Ali Jadbabaie, Horia Mania, Devavrat Shah, Suvrit Sra

We revisit a model for time-varying linear regression that assumes the unknown parameters evolve according to a linear dynamical system.

regression

Federated Optimization of Smooth Loss Functions

no code implementations6 Jan 2022 Ali Jadbabaie, Anuran Makur, Devavrat Shah

Under some assumptions on the loss function, e. g., strong convexity in parameter, $\eta$-H\"older smoothness in data, etc., we prove that the federated oracle complexity of FedLRGD scales like $\phi m(p/\epsilon)^{\Theta(d/\eta)}$ and that of FedAve scales like $\phi m(p/\epsilon)^{3/4}$ (neglecting sub-dominant factors), where $\phi\gg 1$ is a "communication-to-computation ratio," $p$ is the parameter dimension, and $d$ is the data dimension.

Federated Learning

Unifying Epidemic Models with Mixtures

no code implementations7 Jan 2022 Arnab Sarker, Ali Jadbabaie, Devavrat Shah

The model represents time series of cases and fatalities as a mixture of Gaussian curves, providing a flexible function class to learn from data compared to traditional mechanistic models.

Time Series Time Series Analysis

Counterfactual inference for sequential experiments

no code implementations14 Feb 2022 Raaz Dwivedi, Katherine Tian, Sabina Tomkins, Predrag Klasnja, Susan Murphy, Devavrat Shah

Our goal is to provide inference guarantees for the counterfactual mean at the smallest possible scale -- mean outcome under different treatments for each unit and each time -- with minimal assumptions on the adaptive treatment policy.

counterfactual Counterfactual Inference +3

Current Implicit Policies May Not Eradicate COVID-19

no code implementations29 Mar 2022 Ali Jadbabaie, Arnab Sarker, Devavrat Shah

Successful predictive modeling of epidemics requires an understanding of the implicit feedback control strategies which are implemented by populations to modulate the spread of contagion.

Gradient Descent for Low-Rank Functions

no code implementations16 Jun 2022 Romain Cosson, Ali Jadbabaie, Anuran Makur, Amirhossein Reisizadeh, Devavrat Shah

When $r \ll p$, these complexities are smaller than the known complexities of $\mathcal{O}(p \log(1/\epsilon))$ and $\mathcal{O}(p/\epsilon^2)$ of {\gd} in the strongly convex and non-convex settings, respectively.

Network Synthetic Interventions: A Causal Framework for Panel Data Under Network Interference

no code implementations20 Oct 2022 Anish Agarwal, Sarah H. Cen, Devavrat Shah, Christina Lee Yu

We propose an estimator, Network Synthetic Interventions (NSI), and show that it consistently estimates the mean outcomes for a unit under an arbitrary set of counterfactual treatments for the network.

counterfactual

On counterfactual inference with unobserved confounding

no code implementations14 Nov 2022 Abhin Shah, Raaz Dwivedi, Devavrat Shah, Gregory W. Wornell

Given an observational study with $n$ independent but heterogeneous units, our goal is to learn the counterfactual distribution for each unit using only one $p$-dimensional sample per unit containing covariates, interventions, and outcomes.

counterfactual Counterfactual Inference +1

Doubly robust nearest neighbors in factor models

no code implementations25 Nov 2022 Raaz Dwivedi, Katherine Tian, Sabina Tomkins, Predrag Klasnja, Susan Murphy, Devavrat Shah

We consider a matrix completion problem with missing data, where the $(i, t)$-th entry, when observed, is given by its mean $f(u_i, v_t)$ plus mean-zero noise for an unknown function $f$ and latent factors $u_i$ and $v_t$.

counterfactual Counterfactual Inference +1

Matrix Estimation for Individual Fairness

no code implementations4 Feb 2023 Cindy Y. Zhang, Sarah H. Cen, Devavrat Shah

Specifically, we show that using a popular ME method known as singular value thresholding (SVT) to pre-process the data provides a strong IF guarantee under appropriate conditions.

Fairness

A User-Driven Framework for Regulating and Auditing Social Media

no code implementations20 Apr 2023 Sarah H. Cen, Aleksander Madry, Devavrat Shah

In particular, we introduce the notion of a baseline feed: the content that a user would see without filtering (e. g., on Twitter, this could be the chronological timeline).

SAMoSSA: Multivariate Singular Spectrum Analysis with Stochastic Autoregressive Noise

no code implementations NeurIPS 2023 Abdullah Alomar, Munther Dahleh, Sean Mann, Devavrat Shah

However, a theoretical underpinning of multi-stage learning algorithms involving both deterministic and stationary components has been absent in the literature despite its pervasiveness.

Open-Ended Question Answering Time Series +1

Exploiting Observation Bias to Improve Matrix Completion

no code implementations7 Jun 2023 Yassir Jedra, Sean Mann, Charlotte Park, Devavrat Shah

Instead of treating this observation bias as a disadvantage, as is typically the case, the goal is to exploit the shared information between the bias and the outcome of interest to improve predictions.

Matrix Completion

On Computationally Efficient Learning of Exponential Family Distributions

no code implementations12 Sep 2023 Abhin Shah, Devavrat Shah, Gregory W. Wornell

While the traditional maximum likelihood estimator for this class of exponential family is consistent, asymptotically normal, and asymptotically efficient, evaluating it is computationally hard.

Predicting Ground Reaction Force from Inertial Sensors

no code implementations4 Nov 2023 Bowen Song, Marco Paolieri, Harper E. Stewart, Leana Golubchik, Jill L. McNitt-Gray, Vishal Misra, Devavrat Shah

Our aim in this paper is to determine if data collected with inertial measurement units (IMUs), that can be worn by athletes during outdoor runs, can be used to predict GRF with sufficient accuracy to allow the analysis of its derived biomechanical variables (e. g., contact time and loading rate).

Hyperparameter Optimization regression

Auditing for Human Expertise

1 code implementation NeurIPS 2023 Rohan Alur, Loren Laine, Darrick K. Li, Manish Raghavan, Devavrat Shah, Dennis Shung

A rejection of our test thus suggests that human experts may add value to any algorithm trained on the available data, and has direct implications for whether human-AI `complementarity' is achievable in a given prediction task.

A Causal Framework to Evaluate Racial Bias in Law Enforcement Systems

no code implementations22 Feb 2024 Jessy Xinyi Han, Andrew Miller, S. Craig Watkins, Christopher Winship, Fotini Christia, Devavrat Shah

We provide a theoretical characterization and an associated data-driven method to evaluate (a) the presence of any form of racial bias, and (b) if so, the primary source of such a bias in terms of race and criminality.

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